Uncovering relationships between neural activity and behavior represents a critical challenge, one that would benefit from facile tools that can capture complex structures within large datasets. Here we demonstrate a generalizable strategy for capturing such structures across diverse behaviors: Time-REsolved BehavioraL Embedding (TREBLE). Using data from synthetic trajectories, adult and larval Drosophila, and mice we show how TREBLE captures both continuous and discrete behavioral dynamics, can uncover variation across individuals, detect the effects of optogenetic perturbation in unbiased fashion, and reveal structure in pose estimation data. By applying TREBLE to moving mice, and medial entorhinal cortex (MEC) recordings, we show that nearly all MEC neurons encode information relevant to specific movement patterns, expanding our understanding of how navigation is related to the execution of locomotion. Thus, TREBLE provides a flexible framework for describing the structure of complex behaviors and their relationships to neural activity.

中文翻译：

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通过时间分辨流形嵌入灵活地分析动物行为

揭示神经活动与行为之间的关系是一项严峻的挑战，这将得益于可捕获大型数据集中复杂结构的便捷工具。在这里，我们演示了一种捕获各种行为中的此类结构的通用策略：时间分辨行为嵌入（TREBLE）。使用合成轨迹，成年和幼虫果蝇以及小鼠的数据，我们展示了TREBLE如何捕获连续和离散的行为动力学，如何发现个体之间的差异，以无偏见的方式检测光遗传学扰动的影响，并在姿势估计数据中揭示结构。通过将TREBLE应用于运动的小鼠和内侧内嗅皮层（MEC）记录，我们发现几乎所有的MEC神经元都编码与特定运动模式相关的信息，拓宽了我们对导航与运动执行之间的关系的理解。因此，TREBLE提供了一个灵活的框架来描述复杂行为的结构及其与神经活动的关系。